Modeling micronutrients in the ocean: Modeling micronutrients in the ocean:

The case of IronThe case of Iron

Olivier Aumont

Based on the work by: L. Bopp, A. Tagliabue, J.K. Moore, W. Gregg, P. Parekh,

A. Ridgwell, S. Dutkiewicz, D. Archer, L. Weber, C. Voelker, K. Flynn, ...

LOCEAN, Centre IRD de Bretagne, Plouzané, France

Nozaki Periodic TableMacronutrients

Micronutrients

The case of IronThe case of Iron

Iron: Used for many chemical and biological processes in

phytoplankton cells

N metabolism Nitrate and Nitrite reductase Photosynthesis & Respiration Cytochrome, ferrodoxin, … Process Catalyst …

Iron has been demonstrated to play a critical role in large regions of

the ocean

• Limits primary productivity

• Controls species composition

• Trophic structure

Consequence: Iron is the only micro-nutrient that has been explicitely

included in ocean biogeochemical models so far

(Short)(Short) History History

1931: Gran suggested iron could be limiting in the Southern Ocean

1980: First reliable iron measurements in the open ocean

1980’s: Martin showed that iron stimulates phytoplankton growth in

incubations

1993: Ironex I

1997: First 1D biogeochemical models with explicit iron (Loukos et al,

Johnson et al)

2000: First global 3D model with iron (Archer et al)

2007: At least, 6 global biogeochemical models include a description of

the iron cycle

The Iron cycle in the oceanThe Iron cycle in the ocean

Fecoll

Fe(III)’ FeL

Fe(II)’

Fepart

Dissolved iron

Phyto.Bacteria

Zoo.

SedimentsRivers

Dust

(Simple view)(Simple view)

sinkingsinking

sinkingsinking

ObservationsObservations(Design/validate the models)

Iron fertilization experimentsIron fertilization experiments

EisenEx EiFexEisenEx EiFex

SOIREESOIREE

, II, II

SOFeXSOFeX

SEEDSI &II

SERIESSEEDSI &IISEEDSI &II

SERIESSERIES

IronEx IIronEx I

CYCLOPSCYCLOPSFeEPFeEP

+ Natural system, detailed obs., …

- Small scales, HNLC systems, …

Lab experimentsLab experiments

+ Processes, detailed obs., parameters …

- Artificial systems

In situ measurementsIn situ measurements

+ Large spatio-temp. scales, natural system, …

- Coverage, speciation, parameters, …

Iron distributionIron distribution

Dissolved iron (nM)Dissolved iron (nM) 0-50 m0-50 m

Dissolved iron (nM)Dissolved iron (nM) 500-2000 m500-2000 m

OutlineOutline

IntroductionIntroduction

Iron chemistryIron chemistry

Biological uptakeBiological uptake

External sources of ironExternal sources of iron

Past/Future scenariosPast/Future scenarios

Conclusions/thoughtsConclusions/thoughts

I won't discuss about iron cycle in sediments and in dust

Iron chemistry (I)Iron chemistry (I)

From Archer and Johnson (2000)

The simplest iron modelThe simplest iron model

FeT Fepart

ksc

This model is not used anymore

The Johnson modelThe Johnson model

Fe’ Fepart

ksc

FeL

This model is still commonly usedFrom Archer and Johnson (2000)

< 0.6nM

Iron at 2500m (nM)

Iron chemistry (II)Iron chemistry (II)

Adsorption/coagulation modelAdsorption/coagulation model

Fe’ Fepart

FeL

(Aumont and Bopp, 2006;Moore and Braucher, 2007)

kcoag

kads

Des

orpt

ion/

rem

in.

Better agreement but:Better agreement but:

- Predicted iron concentrations too uniform in the deep ocean

- Parameters at the surface and in the deep ocean differ significantly

- Desorption improves model results but is not demonstrated

Iron SpeciationIron Speciation

Iron speciation modelsIron speciation models

Fe(III)' Fepart

FeLb

Des

orpt

ion/

rem

in.

Fe(II)'

FeLa

(Tagliabue and Arrigo, 2006; Weber et al., 2005;2007)

Tagliabue and Arrigo, 2006

(Tagliabue et al., 2007, sub.)

Iron speciation does matter !! Iron speciation does matter !!

Impacts restricted to the upper ocean

Expensive, many unconstrained parameters and processes

Iron Chemistry: Current state/challengesIron Chemistry: Current state/challenges

What we have learnt from modelsWhat we have learnt from models

Fecoll

Fe(III)’ FeL

Fe(II)’

Fepart

Critical for iron distribution in the deep ocean

Critical for PP and surface Iron

Future challengesFuture challenges

Dynamics of Iron colloids (Thorium, DOM analysis, ...)

Bioavailability of the different forms of operationnally defined dissolved iron

Ligands

Phytoplankton growthPhytoplankton growth

Most biogeochemical models: NPZD-type modelsMost biogeochemical models: NPZD-type models

Phytoplankton growthPhytoplankton growth

N1, N2, ...N1, N2, ... P1, P2, ...P1, P2, ...

D1, D2, ...D1, D2, ... Z1, Z2, ...Z1, Z2, ...

μ = μM L

N (1-exp(- (Chl/C) E/ μ

M)

IronIron

LLNN : Quota or Monod Approach (see for instance the work by Flynn and coauthors : Quota or Monod Approach (see for instance the work by Flynn and coauthors

for discussion on both approaches)for discussion on both approaches)

Constant parameters for iron limitation (except in Flynn, 2001)Constant parameters for iron limitation (except in Flynn, 2001)

Fe/C ratioFe/C ratio

First iron models : constant Fe/C ratios (5-10First iron models : constant Fe/C ratios (5-10mol/mol)

Currently, most (both quota and Monod) models have variable Fe/CCurrently, most (both quota and Monod) models have variable Fe/C

(Loukos et al., 1997; Lefèvre and Watson, 1999 ; Archer and Johnson, 2000; ...)

No luxury uptake, No Fe adsorbed onto the cell walls

Values representative of the open ocean

(Moore et al, 2002)(Moore et al, 2002)

Iron LimitationIron Limitation

All models predict similar patterns for Fe limitationAll models predict similar patterns for Fe limitation

(Aumont and Bopp, 2006) (Moore et al., 2004)Diatoms limiting factors

They reproduce the main characteritics of HNLC regions but ...They reproduce the main characteritics of HNLC regions but ...

1D models and obs. suggest higher K

Iron is not the whole story : light !!!

(Gregg et al., 2003)

Other componentsOther components

Most of the modeling work has concentrated on phytoplanktonMost of the modeling work has concentrated on phytoplankton

Detailed mechanistic models: Flynn, 2001 ; Armstrong 1999 ; ...

In comparison, other components have received much less attentionIn comparison, other components have received much less attention

Bacteria are not modeled. Bacteria are not modeled.

Observations: in competition with phytoplankton for iron

Zooplankton role in iron cycle is neglectedZooplankton role in iron cycle is neglected

Constant Fe/C ratios or passively controled by its diet

Never iron limited, nor affected by the Fe/C of its preys. Ideas from the work

by Mitra et al. (2006,2007)

Iron acquisition only from organic matter (preys) whereas studies have

shown colloids consumption (e.g., Chen and Wang, 2001)

Sediment mobilizationRivers

Dust deposition

Hydrothermal vents

External sources of ironExternal sources of iron

Dust depositionDust deposition

Historically, the external source which has received the first and main Historically, the external source which has received the first and main

attentionattention

(Jickells et al., 2005)

Large uncertainties in dust deposition to the ocean: 290-430 Mt/year

Dust deposition in modelsDust deposition in models

All models include this source, but with very simple parameterizationsAll models include this source, but with very simple parameterizations

Iron is a constant fraction of dust, typically ~3.5%

Solubility is constant, typically between 1% and 5%

Monthly-mean climatological fields

Solubility is not constant (time/space)Solubility is not constant (time/space)

(From Hand et al., 2004)

Dust dissolves not only at the surface Dust dissolves not only at the surface (Moore et al., 2004; Aumont and Bopp, 2006)(Moore et al., 2004; Aumont and Bopp, 2006)

Weak sensitivity to increased iron flux

Increased PP in HNLC region balanced by larger oligotrophic regions and scavenging

Role of Dust DepositionRole of Dust Deposition

Models have been used to estimate the contribution of aelion iron to Models have been used to estimate the contribution of aelion iron to new ironnew iron

Dust scenarios or estimating its impact on PPDust scenarios or estimating its impact on PP

• About 20% to 30% of new iron in the euphotic zone comes from dust

(Archer and Johnson, 2000; Moore et al, 2002 ; Aumont et al., 2003)

• But new iron is not necessarily completely used by phytoplankton

Weak sensitivity to decreased iron flux at the beginning

Strong iron flux over oligotrophic regions

Strong sensitivity to decreased iron flux over long timescales

Iron scavenging not balanced anymore by dust supply

Temporal variability of dust depositionTemporal variability of dust deposition

Dust deposition extremely variable on all timescalesDust deposition extremely variable on all timescales

Dust scenarios or estimating its impact on PPDust scenarios or estimating its impact on PP

Iron deposition at BATS (g Fe/d/m2)

(from INCA2 atmospheric model)

Iron relative variability Chlorophyll absolute variability (mgChl/m3)

Problem : iron variability seems underestimated with 1-2% solubility

Sediment mobilizationSediment mobilization

A significant source to the oceanA significant source to the ocean

Sediments in modelsSediments in models

• Resuspension and diffusion generates high Fe in coastal regions (> 5nM)

• Influence far offshore along some transects (S. Polar Frontal zone, N. Pacific)

(From Moore et al., 2007)

Only very few models include this source(Moore et al., 2004; Aumont and Bopp, 2006;Tagliabue and Arrigo, 2006; Moore and Braucher,

2007)

Estimated magnitude : ~2.10Estimated magnitude : ~2.101010 mol Fe/yr (very, very uncertain !!!!) mol Fe/yr (very, very uncertain !!!!)

Without it, models are much too sensitive to variations in dust deposition

External sources: ThoughtsExternal sources: Thoughts

Dust depositionDust deposition

The main unknown is the solubility (the rest is second order ...)

Processing in surface and deep waters

Dust is not the only major source

Sediment mobilizationSediment mobilization

Potentially as important as dust

We know very very little !!

Oxic/anoxic, Diffusion/Suspension/Irrigation, ...

Rivers ??Rivers ??

Hydrothermal vents ??Hydrothermal vents ??

LGM: The iron hypothesisLGM: The iron hypothesis

Atmospheric pCO2 about 80-100 ppmv lower than preindustrial levelsAtmospheric pCO2 about 80-100 ppmv lower than preindustrial levels

The Iron hypothesis The Iron hypothesis (Martin, 1990)(Martin, 1990)

Ice cores suggest higher dust deposition in the Southern Ocean

Higher aeolian input ⇒ enhanced PP ⇒ enhanced C sequestration

Models Models ?

A predicted CO2 drawdown between ~8 and ~30 ppmv(Archer and Johnson, 2000; Bopp et al., 2003; Parekh et al., 2006)

Compensation between enhanced PP in HNLC regions and decreased PP

due to lower N, P, Si levels

An other Iron effect ?An other Iron effect ?

Lower sea level = lower sediment mobilization

Maximum effect = +17 ppmv (Aumont et al., 2007, in prep.)

Future climateFuture climate

Dust deposition is predicted to decreaseDust deposition is predicted to decrease

40% decrease between 2000 and 2100 (Mahowald et al., 2006)

Impact on the C cycleImpact on the C cycle

Ocean sink is reduced by 0.5 Pg C/yr (Moore et al., 2006)

Atmospheric pCO2 increased by up to 100 atm (Parekh et al., 2006)

With Sediments, the impact is reduced to 0.2 Pg C/yr (Tagliabue et al., 2007,

sub.)

ConclusionsConclusions

Models proved to be useful despite the oversimplications Models proved to be useful despite the oversimplications

They are able to reproduce the basic features of the iron cycleThey are able to reproduce the basic features of the iron cycle

Major uncertainties in the iron cycleMajor uncertainties in the iron cycle

Scavenging/coagulation. In particular, what is the role of the colloids?Scavenging/coagulation. In particular, what is the role of the colloids?

A lot to be learn from other metals, especially Th

Characteristics of the ligands

Biological processes. What is the role of zooplankton/bacteria?Biological processes. What is the role of zooplankton/bacteria?

Iron on particles in the deep ocean

External sources of Iron. What is the role of sediments?External sources of Iron. What is the role of sediments?

Solubility of aelian iron

– Data required both from process studies and from the field (Obvious !!!)Data required both from process studies and from the field (Obvious !!!)

Speciation of iron (truly dissolved, colloidal, particulate, ...)Speciation of iron (truly dissolved, colloidal, particulate, ...)